CN116389313B - Detection system capable of detecting circuit in real time and remotely - Google Patents
Detection system capable of detecting circuit in real time and remotely Download PDFInfo
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- 238000004891 communication Methods 0.000 claims abstract description 109
- 230000005856 abnormality Effects 0.000 claims abstract description 45
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- 238000012360 testing method Methods 0.000 claims description 6
- 238000007689 inspection Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 238000000034 method Methods 0.000 abstract description 23
- 238000004590 computer program Methods 0.000 description 7
- 238000012423 maintenance Methods 0.000 description 7
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
- H04L43/0811—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking connectivity
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/142—Network analysis or design using statistical or mathematical methods
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/50—Reducing energy consumption in communication networks in wire-line communication networks, e.g. low power modes or reduced link rate
Abstract
The invention relates to a detection system capable of detecting lines in real time and remotely, which establishes a flow model of each line group through historical flow data, and then judges whether the line group with abnormal flow exists at the current time based on the flow data at the current time and the flow model; when there is a line packet with abnormal flow, the flow abnormality is detected for a plurality of communication lines in the line packet with abnormal flow. According to the method, the historical flow data of each line are analyzed, a flow model of the corresponding relation between time and flow is established, whether the line group has flow abnormality is judged according to the flow data of the current time, and finally each communication line in the line group with flow abnormality is detected, so that automatic detection of the line is completed, and the detection efficiency is high when the line can be detected remotely.
Description
Technical Field
The invention relates to the technical field of network operation and maintenance, in particular to a detection system capable of detecting a line in real time and remotely.
Background
The communication machine room is a place or a place where communication equipment and facilities are installed and the operation conditions can be met. Generally, the system can be divided into a special machine room and a comprehensive machine room. The special machine room refers to a special place for intensively installing the power communication equipment in operation, and is divided into an independent communication machine room, a transformer substation and a communication machine room in a power plant. The comprehensive machine room refers to a machine room or a secondary equipment room shared by the power communication equipment and other secondary equipment.
Inside the communication room there are a large number of communication lines, such as optical cables, coaxial cables, network cables, etc. In order to ensure the normal operation of the machine room, the communication line needs to be checked regularly. For the detection of the communication line, the maintainer carries the tester to detect the connectivity of the communication line at present, so that the detection efficiency is low, and the maintainer needs to go to the site to work, thereby wasting time and labor.
Disclosure of Invention
Therefore, the present invention is directed to a detection system capable of detecting a line in real time and remotely, so as to solve the problems of low detection efficiency and requiring maintenance personnel to go to the field for operation in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the invention relates to a detection system capable of detecting a line in real time and remotely, which comprises:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical flow data of a plurality of line groups and flow data at the current time, each line group comprises a plurality of communication lines, and the flow distribution characteristics of the plurality of communication lines in the same line group are the same;
the model building module is used for building a flow model of each line group based on the historical flow data, wherein each flow model represents the corresponding relation between the flow of the corresponding line group and time;
the first detection module is used for judging whether line grouping with abnormal flow exists at the current time or not based on the flow data at the current time and the flow model;
and the second detection module is used for detecting the flow abnormality of a plurality of communication lines in the line group with the flow abnormality when the line group with the flow abnormality exists.
In an embodiment of the present application, the method further includes grouping the plurality of communication lines as follows:
acquiring historical flow data of a plurality of communication lines, wherein the historical flow data comprises historical time points and flow data corresponding to the historical time points;
constructing a flow distribution characteristic of each communication line based on the historical time point and flow data corresponding to the historical time point, wherein the flow distribution characteristic characterizes the relationship between the flow of the communication line and time;
and grouping the plurality of communication lines based on the traffic distribution characteristics to obtain a plurality of line groups.
In an embodiment of the present application, the constructing a flow distribution feature of each communication line based on the historical time point and the flow data corresponding to the historical time point includes:
mapping the historical time point and the flow data corresponding to the historical time point into a plurality of time periods to obtain a plurality of period flow data, wherein each time period comprises a plurality of time periods;
summarizing the flow data corresponding to the time periods to obtain the average value and the variance of the flow data of all the historical time points corresponding to each time period in the time periods;
taking a time period with variance smaller than a preset threshold value as a target time period, and constructing a flow range corresponding to the target time period based on the average value and variance of flow data of the target time period;
and constructing the flow distribution characteristics of the communication line based on each target time period and the flow range corresponding to each target time period.
In an embodiment of the present application, the mathematical expression of the flow range corresponding to the target time period is:wherein->Is->Average value of flow data for each target period, +.>Is->Variance of flow data for each target time period.
In an embodiment of the present application, the distribution of the target time periods corresponding to the multiple communication lines in the same line packet is consistent.
In an embodiment of the present application, establishing a flow model for each line packet based on the historical flow data includes:
summing the flow ranges of the same target time periods corresponding to the communication lines in each line group to obtain the total flow range of all the target time periods;
confirming the lower limit value of the total flow range of a plurality of target time periods, and constructing abnormal alarm values of the plurality of target time periods based on the lower limit value;
a flow model is derived based on the plurality of target time periods and the anomaly alert values for the plurality of target time periods.
In one embodiment of the present application, the anomaly alert valueWherein->Is a scale factor->Less than or equal to 1 @, @>For the lower limit value, +.>To adjust the parameters.
In an embodiment of the present application, determining whether a line packet with a traffic abnormality exists at the current time based on the traffic data at the current time and the traffic model includes:
determining a time period corresponding to the flow data of the current time;
when the time period corresponding to the flow data of the current time is a target time period and the value of the flow data of the current time is smaller than an abnormal alarm value of the target time period of the corresponding line packet, judging that the corresponding line packet has flow abnormality; otherwise, judging that the corresponding line packet has no traffic abnormality.
In an embodiment of the present application, detecting traffic anomalies of a plurality of communication lines in the line packet with traffic anomalies includes:
acquiring flow data of each communication line in the line group with abnormal flow at the current time;
and comparing the flow data of each communication line in the line group with a preset flow threshold value at the current time, and judging that the communication line has the flow abnormality when the value of the flow data of any communication line at the current time is smaller than the preset flow threshold value.
In an embodiment of the present application, further includes:
and when any communication line has traffic abnormality, performing connectivity test on the communication line with traffic abnormality.
The application also provides a detection method capable of detecting the circuit in real time and remotely, which comprises the following steps:
acquiring historical flow data of a plurality of line groups and flow data at the current time, wherein each line group comprises a plurality of communication lines, and the flow distribution characteristics of the communication lines in the same line group are the same;
establishing a flow model of each line group based on the historical flow data, wherein each flow model represents the corresponding relation between the flow of the corresponding line group and time;
judging whether line grouping with abnormal flow exists at the current time or not based on the flow data at the current time and the flow model;
and when the line packet with the abnormal flow exists, detecting the abnormal flow of the plurality of communication lines in the line packet with the abnormal flow.
In another aspect of the present application, there is also provided a server, including:
a processor; and
a memory in which a program is stored,
wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the method of any of the above.
In another aspect of the present application, there is also provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of the above.
The beneficial effects of the invention are as follows: according to the detection system capable of detecting the lines in real time and remotely, a flow model of each line group is established through historical flow data, and then whether the line group with abnormal flow exists at the current time is judged based on the flow data at the current time and the flow model; when there is a line packet with abnormal flow, the flow abnormality is detected for a plurality of communication lines in the line packet with abnormal flow. According to the method, the historical flow data of each line are analyzed, a flow model of the corresponding relation between time and flow is established, whether the line group has flow abnormality is judged according to the flow data of the current time, and finally each communication line in the line group with flow abnormality is detected, so that automatic detection of the line is completed, and the detection efficiency is high when the line can be detected remotely.
Drawings
The invention is further described below with reference to the accompanying drawings and examples:
FIG. 1 is an application scenario diagram of a real-time, remote line-detectable detection system of the present application;
FIG. 2 is a block diagram of a detection system capable of real-time, remote detection of lines as shown in one embodiment of the present application;
fig. 3 is a flow chart illustrating grouping of multiple communication lines in an embodiment of the present application;
FIG. 4 is a flow chart illustrating the construction of flow distribution features in an embodiment of the present application;
fig. 5 is a flow chart illustrating the establishment of a flow model in an embodiment of the present application.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the layers related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the layers in actual implementation, and the form, number and proportion of the layers in actual implementation may be arbitrarily changed, and the layer layout may be more complex.
In the following description, numerous details are discussed to provide a more thorough explanation of embodiments of the present invention, however, it will be apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details.
The detection system capable of detecting the line in real time and remotely is applied to the field of computer management, and an execution object can be a computer, a mobile terminal or a server.
Fig. 1 is an application scenario diagram of a real-time and remote line detection system in the present application, as shown in fig. 1, in this scenario, a communication structure of a communication room is shown, which mainly includes a core switch 110, a convergence switch 120, and an access layer switch 130. Wherein the core switch is connected to the internet through a firewall (e.g., gateway device) and is also connected to a database server or the like. Each core switch 110 is connected to a plurality of aggregation switches 120, and each aggregation switch 120 is connected to a plurality of access stratum switches 130. A plurality of user devices 140 are then connected with each access stratum switch 130 as a node. That is, each network interface of the core switch 110, each network interface of the aggregation switch 120, and each network interface of the access layer switch 130 are connected to a plurality of user devices.
The method and the device can be applied to the scene, and each network interface of the core switch, each network interface of the aggregation switch and the communication line corresponding to each network interface of the access layer switch are automatically detected.
Fig. 2 is a block diagram of a detection system capable of detecting a line in real time and remotely, according to an embodiment of the present application, as shown in fig. 2: the detection system capable of detecting a line in real time and remotely in this embodiment includes:
an obtaining module 210, configured to obtain historical traffic data of a plurality of line packets and traffic data at a current time, where each line packet includes a plurality of communication lines, and traffic distribution characteristics of the plurality of communication lines in the same line packet are the same;
the method comprises the steps of analyzing the flow distribution characteristics of each communication line in a machine room in advance, and dividing the communication lines with the same flow distribution characteristics into the same line group, so that a plurality of line groups are obtained. The flow distribution characteristics include a correspondence between time and flow. For example, with each natural day as a time period, 21:00-22:00 are peak periods of flow, the flow can reach nGB/s, where 21:00-22:00, nGB/s is a part of the flow distribution feature, and the complete flow distribution feature includes complete flow distribution in one time period.
In this embodiment, by dividing the communication lines having the same flow distribution characteristics into the same line packet, the flow change of the line packet can be made regular. To facilitate subsequent processing.
Fig. 3 is a flowchart of grouping a plurality of communication lines according to an embodiment of the present application, and as shown in fig. 3, the plurality of communication lines are grouped through steps S310 to S330:
step S310, historical flow data of a plurality of communication lines are obtained, wherein the historical flow data comprises historical time points and flow data corresponding to the historical time points;
the flow data corresponding to the historical time point refers to the data quantity which can be reached every second. By collecting flow data corresponding to a plurality of historical time points corresponding to each communication line, the relation between time corresponding to each communication line and the flow data can be analyzed to obtain flow distribution characteristics.
Step S320, constructing a flow distribution characteristic of each communication line based on the historical time point and the flow data corresponding to the historical time point, wherein the flow distribution characteristic characterizes the relation between the flow of the communication line and time;
the flow distribution characteristic is embodied by the distribution rule of the flow and time of each communication line. Thus, if there is no rule in the flow rate for a certain period of time, it is interpreted that there is no rule in the response for this period of time.
Fig. 4 is a flowchart illustrating the construction of the flow distribution feature according to an embodiment of the present application, and as shown in fig. 4, based on the foregoing concept, the method for constructing the flow distribution feature includes:
step S410, mapping the historical time points and the flow data corresponding to the historical time points into a plurality of time periods to obtain a plurality of period flow data, wherein each time period comprises a plurality of time periods;
wherein each time period may be one week or one day, and the time period may be a time continuous interval of 1-3 hours, such as a time period of 21:00-23:00.
Step S420, summarizing the flow data corresponding to the time periods to obtain the average value and the variance of the flow data of all the historical time points corresponding to each time period in the time periods;
in this embodiment, after the flow data corresponding to the multiple time periods are summarized, multiple data samples corresponding to each time period can be obtained. After averaging and variance are performed on the multiple data samples for each time period, a regularity analysis may be performed thereon. For example, there are 200 data samples from 21:00 to 23:00 over all time periods (e.g., nature days), and by averaging the 200 data samples and variance, the average flow data is xGB/s and the variance is y.
Step S430, taking a time period with variance smaller than a preset threshold value as a target time period, and constructing a flow range corresponding to the target time period based on the average value and variance of flow data of the target time period;
the variance may reflect the stability of the data samples corresponding to each time period, and when the variance of any time period is smaller than a preset threshold, the flow data in the time period is considered to be stable enough and have regularity. At this time, this period is taken as a target period in which a law exists, and the average value thereof may approximately reflect the law characteristics thereof.
Step S440, constructing a flow distribution characteristic of the communication line based on each target time period and the flow range corresponding to each target time period.
The mathematical expression of the flow range corresponding to the target time period is as follows:wherein->Is->Average value of flow data for each target period, +.>Is->Variance of flow data for each target time period.
In this embodiment, the standard deviation and the average value are used to construct the flow range corresponding to the target time period, and the flow distribution feature is constructed based on the time period and the flow range corresponding to the time period.
And step S330, grouping the plurality of communication lines based on the flow distribution characteristics to obtain a plurality of line groups.
Wherein, the distribution condition of the target time periods corresponding to a plurality of communication lines in the same line group is consistent.
Since the target time periods for the communication lines in the same line packet are identical, when the time reaches within the target time period, the corresponding traffic data should be in a stable range section. It is thus possible to determine whether there is a traffic abnormality in the corresponding line packet through this range section, and if there is a traffic abnormality, it is then possible to determine that there is a traffic abnormality in one or more communication lines inside. Thus, line groups with abnormal flow are positioned, and maintenance personnel are helped to find fault points.
In addition, the method and the device can reduce the complexity of detection and can realize automatic detection of all communication lines by fewer devices by dividing a plurality of line groups, wherein each line group comprises a plurality of communication lines with the same flow distribution characteristics. The detection efficiency and the automation level are greatly improved.
A model building module 220, configured to build a flow model of each line packet based on the historical flow data, where each flow model characterizes a corresponding relationship between flow and time of the corresponding line packet;
the flow model reflects the corresponding relation between the flow of each line group and time, and each line group establishes a corresponding flow model based on the flow distribution characteristics of the line group. So that a one-to-one correspondence is made to each line packet.
FIG. 5 is a flow chart illustrating a flow modeling process according to an embodiment of the present application, where the flow modeling process may include:
step S510, summing the flow ranges of the same target time periods corresponding to the communication lines in each line group to obtain the total flow range of all the target time periods;
wherein the traffic distribution characteristics of the communication lines due to each line packet are identical, i.e. the distribution situation of the target time period is uniform. Thus, the total flow range corresponding to each target time period in the line packet is obtained by summing the flow ranges corresponding to each target time period in the line packet, and the total flow range corresponding to each target time period in the line packet is stable. For example, the flow range corresponding to a certain target period includes (a 1, b 1), (a 2, b 2), (a 3, b 3), and then the corresponding total flow range should be (a1+a2+a3, b1+b2+b3).
Step S520, confirming the lower limit value of the total flow range of a plurality of target time periods, and constructing an abnormal alarm value of the plurality of target time periods based on the lower limit value; wherein the abnormal alarm valueWherein->Is a scale factor->Less than or equal to 1 @, @>For the lower limit value, +.>To adjust the parameters.
In this embodiment, the individual data is easy to have specificity, for example, very close to the lower limit value or even slightly lower than the lower limit value, but no abnormal flow exists at this time. Therefore, in the present embodiment, in order to avoid the above situation, the judgment accuracy of the abnormality alert value is increased by setting the scale factor smaller than or equal to 1. And meanwhile, an adjusting parameter is added, so that maintenance personnel can adjust the abnormal alarm value according to actual conditions.
In this embodiment, only the case of a sudden drop in the flow rate of the communication line is considered, and therefore, when there is a failure in the communication line, there is generally only a case where the flow rate drops or even communication is not possible. Therefore, in the present application, an abnormal alarm value is constructed with the lower limit value of the overall flow range, and the abnormal alarm value is used as the reference value of the corresponding time period.
In step S530, a flow model is obtained based on the plurality of target time periods and the abnormal alarm values of the plurality of target time periods.
The last flow model includes one or more target time periods and abnormal alarm values of the target time periods.
A first detection module 230, configured to determine whether a line packet with abnormal flow exists at the current time based on the flow data at the current time and the flow model;
wherein, the target time period of the flow data and the corresponding abnormal alarm value are determined based on the current time. And judging whether the line packet has traffic abnormality or not based on the corresponding abnormality alarm value.
Specifically, the judging process includes:
judging whether the current time has the line grouping with abnormal flow based on the flow data of the current time and the flow model, comprising the following steps:
determining a time period corresponding to flow data of the current time;
when the time period corresponding to the flow data in the current time is a target time period and the value of the flow data in the current time is smaller than the abnormal alarm value of the target time period of the corresponding line packet, judging that the corresponding line packet has flow abnormality; otherwise, judging that the corresponding line packet has no traffic abnormality.
And the second detection module 240 is configured to detect, when the line packet with the traffic abnormality exists, the traffic abnormality for a plurality of communication lines in the line packet with the traffic abnormality.
The process of detecting the plurality of communication cables in the line group with abnormal flow comprises the following steps:
acquiring flow data of each communication line in the line group with abnormal flow at the current time;
and comparing the flow data of each communication line in the line group with a preset flow threshold value at the current time, and judging that the communication line has the flow abnormality when the value of the flow data of any communication line at the current time is smaller than the preset flow threshold value.
Wherein the traffic data suddenly drops to a very low level, even close to zero, in the presence of connectivity anomalies on the communication line. Therefore, in the present embodiment, the communication line is determined by setting one flow rate threshold. The method can quickly and effectively judge whether the communication line has traffic abnormality.
In an embodiment of the present application, further includes:
and when any communication line has traffic abnormality, performing connectivity test on the communication line with traffic abnormality.
The connectivity test may include PING related network address, sending test packets to test transmission speed, and so on. After determining that a traffic anomaly exists in the communication line, the above or other means may be employed to determine a particular problem. Thereby automatically determining some obvious problems. If the judgment is impossible, the related information is sent to the maintainer, so that the maintainer is informed to go to the site for means investigation.
The process can help maintenance personnel to carry out routine inspection, the degree of automation is high, and labor is saved. Meanwhile, when problems exist, the simple problems are primarily judged, and when the problems which cannot be judged are encountered, maintenance personnel can be helped to locate, so that the investigation range of the maintenance personnel is shortened.
According to the detection system capable of detecting the lines in real time and remotely, a flow model of each line group is established through historical flow data, and then whether the line group with abnormal flow exists at the current time is judged based on the flow data at the current time and the flow model; when there is a line packet with abnormal flow, the flow abnormality is detected for a plurality of communication lines in the line packet with abnormal flow. According to the method, the historical flow data of each line are analyzed, a flow model of the corresponding relation between time and flow is established, whether the line group has flow abnormality is judged according to the flow data of the current time, and finally each communication line in the line group with flow abnormality is detected, so that automatic detection of the line is completed, and the detection efficiency is high when the line can be detected remotely.
The application also provides a detection method capable of detecting the circuit in real time and remotely, which comprises the following steps:
acquiring historical flow data of a plurality of line groups and flow data at the current time, wherein each line group comprises a plurality of communication lines, and the flow distribution characteristics of the communication lines in the same line group are the same;
establishing a flow model of each line group based on the historical flow data, wherein each flow model represents the corresponding relation between the flow of the corresponding line group and time;
judging whether line grouping with abnormal flow exists at the current time or not based on the flow data at the current time and the flow model;
and when the line packet with the abnormal flow exists, detecting the abnormal flow of the plurality of communication lines in the line packet with the abnormal flow.
According to the detection method capable of detecting the lines in real time and remotely, a flow model of each line group is established through historical flow data, and then whether the line group with abnormal flow exists at the current time is judged based on the flow data at the current time and the flow model; when there is a line packet with abnormal flow, the flow abnormality is detected for a plurality of communication lines in the line packet with abnormal flow. According to the method, the historical flow data of each line are analyzed, a flow model of the corresponding relation between time and flow is established, whether the line group has flow abnormality is judged according to the flow data of the current time, and finally each communication line in the line group with flow abnormality is detected, so that automatic detection of the line is completed, and the detection efficiency is high when the line can be detected remotely.
The present embodiment also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements any of the methods of the present embodiments, wherein the method is the execution logic of the present system.
The embodiment also provides an electronic terminal, including: a processor and a memory;
the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory, so that the terminal executes any one of the methods in the embodiment.
The computer readable storage medium in this embodiment, as will be appreciated by those of ordinary skill in the art: all or part of the steps for implementing the method embodiments described above may be performed by computer program related hardware. The aforementioned computer program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface, where the memory and the communication interface are connected to the processor and the transceiver and complete communication with each other, the memory is used to store a computer program, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer program, so that the electronic terminal performs each step of the above method.
In this embodiment, the memory may include a random access memory (Random Access Memory, abbreviated as RAM), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processing, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field-programmable gate arrays (Field-Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In the above embodiments, while the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art in light of the foregoing description. The embodiments of the invention are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (10)
1. A detection system for detecting a line in real time and remotely, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical flow data of a plurality of line groups and flow data at the current time, each line group comprises a plurality of communication lines, and the flow distribution characteristics of the plurality of communication lines in the same line group are the same;
the model building module is used for building a flow model of each line group based on the historical flow data, wherein each flow model represents the corresponding relation between the flow of the corresponding line group and time;
the first detection module is used for judging whether line grouping with abnormal flow exists at the current time or not based on the flow data at the current time and the flow model;
and the second detection module is used for detecting the flow abnormality of a plurality of communication lines in the line group with the flow abnormality when the line group with the flow abnormality exists.
2. The system for real-time, remote line detection according to claim 1, further comprising grouping the plurality of communication lines by:
acquiring historical flow data of a plurality of communication lines, wherein the historical flow data comprises historical time points and flow data corresponding to the historical time points;
constructing a flow distribution characteristic of each communication line based on the historical time point and flow data corresponding to the historical time point, wherein the flow distribution characteristic characterizes the relationship between the flow of the communication line and time;
and grouping the plurality of communication lines based on the traffic distribution characteristics to obtain a plurality of line groups.
3. The system for real-time and remote line detection according to claim 2, wherein the construction of the flow distribution feature of each communication line based on the historical time points and the flow data corresponding to the historical time points comprises:
mapping the historical time point and the flow data corresponding to the historical time point into a plurality of time periods to obtain a plurality of period flow data, wherein each time period comprises a plurality of time periods;
summarizing the flow data corresponding to the time periods to obtain the average value and the variance of the flow data of all the historical time points corresponding to each time period in the time periods;
taking a time period with variance smaller than a preset threshold value as a target time period, and constructing a flow range corresponding to the target time period based on the average value and variance of flow data of the target time period;
and constructing the flow distribution characteristics of the communication line based on each target time period and the flow range corresponding to each target time period.
4. The system for real-time and remote line detection according to claim 3, wherein the mathematical expression of the flow range corresponding to the target time period is:wherein->Is->Average value of flow data for each target period, +.>Is->Variance of flow data for each target time period.
5. The system for real-time and remote line detection according to claim 3, wherein the distribution of the target time periods corresponding to the plurality of communication lines in the same line group is uniform.
6. A real-time, remotely monitorable line inspection system according to claim 3 and wherein establishing a flow model for each line packet based on said historical flow data comprises:
summing the flow ranges of the same target time periods corresponding to the communication lines in each line group to obtain the total flow range of all the target time periods;
confirming the lower limit value of the total flow range of a plurality of target time periods, and constructing abnormal alarm values of the plurality of target time periods based on the lower limit value;
a flow model is derived based on the plurality of target time periods and the anomaly alert values for the plurality of target time periods.
7. The system for real-time and remote line detection according to claim 6, wherein the anomaly alert valueWherein->Is a scale factor->Less than or equal to 1 @, @>For the lower limit value, +.>To adjust the parameters.
8. The system for real-time and remote line detection according to claim 6, wherein determining whether a line packet having a traffic abnormality at a current time based on the traffic data at the current time and the traffic model comprises:
determining a time period corresponding to the flow data of the current time;
when the time period corresponding to the flow data of the current time is a target time period and the value of the flow data of the current time is smaller than an abnormal alarm value of the target time period of the corresponding line packet, judging that the corresponding line packet has flow abnormality; otherwise, judging that the corresponding line packet has no traffic abnormality.
9. The real-time, remotely monitorable line inspection system according to claim 3 and wherein the flow anomaly inspection of the plurality of communication lines within the flow anomaly line packet comprises:
acquiring flow data of each communication line in the line group with abnormal flow at the current time;
and comparing the flow data of each communication line in the line group with a preset flow threshold value at the current time, and judging that the communication line has the flow abnormality when the value of the flow data of any communication line at the current time is smaller than the preset flow threshold value.
10. The real-time, remote line-detectable detection system of claim 9, further comprising:
and when any communication line has traffic abnormality, performing connectivity test on the communication line with traffic abnormality.
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